Contribution of soil respiration to the global carbon equation

Mechanism of clay mineral modified biochar simultaneously immobilizes heavy metals and reduces soil carbon emissions

Heavy metal pollution in farmland soil has become increasingly severe, and multi-element composite pollution has brought enormous harm to human production and life. Environmental changes in cold regions (such as freeze-thaw cycles and dry-wet alternations) may increase the potential physiological toxicity of heavy metals and exacerbate pollution risks. In order to reveal the effectiveness of sepiolite modified biochar in the remediation of the soil contaminated with lead (Pb), cadmium (Cd), and chromium (Cr), the rice husk biochar pyrolyzed at 500 and 800 °C were selected for remediation treatment (denoted as BC500 and BC800). Meanwhile, different proportions of sepiolite were used for modification (biochar: sepiolite = 1: 0.5 and 1: 1), denoted as MBC500/MBC800 and HBC500/HBC800, respectively. The results showed that modified biochar with sepiolite can effectively improve the immobilization of heavy metals. Under natural conservation condition, the amount of diethylenetriaminepentaacetic acid (DTPA) extractable Pb in BC500, MBC500, and HBC500 decreased by 5.95, 12.39, and 13.55%, respectively, compared to CK. Freeze-thaw cycles and dry-wet alternations activated soil heavy metals, while modified biochar increased adsorption sites and oxygen-containing functional groups under aging conditions, inhibiting the fractions transformation of heavy metals. Furthermore, freeze-thaw cycles promoted the decomposition and mineralization of soil organic carbon (SOC), while sepiolite hindered the release of active carbon through ion exchange and adsorption complexation. Among them, and the soil dissolved organic carbon (DOC) content in HBC800 decreased by 49.39% compared to BC800. Additionally, the high-temperature pyrolyzed biochar (BC800) enhanced the porosity richness and alkalinity of material, which effectively inhibited the migration and transformation of heavy metals compared to BC500, and reduced the decomposition of soil DOC.

Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia

Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests' gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.

The forest, the cicadas and the holey fluxes: Periodical cicada impacts on soil respiration depends on tree mycorrhizal type

The emergence of billions of periodical cicadas affects plant and animal communities profoundly, yet little is known about cicada impacts on soil carbon fluxes. We investigated the effects of Brood X cicadas (Magicicada septendecim, M. cassinii and M. septendeculain) on soil CO2 fluxes (RS ) in three Indiana forests. We hypothesized RS would be sensitive to emergence hole density, with the greatest effects occurring in soils with the lowest ambient fluxes. In support of our hypothesis, RS increased with increasing hole density and greater effects were observed near AM-associating trees (which expressed lower ambient fluxes) than near EcM-associating trees. Additionally, RS from emergence holes increased the temperature sensitivity (Q10 ) of RS by 13%, elevating the Q10 of ecosystem respiration. Brood X cicadas increased annual RS by ca. 2.5%, translating to an additional 717 Gg of CO2 across forested areas. As such, periodical cicadas can have substantial effects on soil processes and biogeochemistry.

Contribution of microbial activity and vegetation cover to the spatial distribution of soil respiration in mountains

The patterns of change in bioclimatic conditions determine the vegetation cover and soil properties along the altitudinal gradient. Together, these factors control the spatial variability of soil respiration (R_S) in mountainous areas. The underlying mechanisms, which are poorly understood, shape the resulting surface CO₂ flux in these ecosystems. We aimed to investigate the spatial variability of R_S and its drivers on the northeastern slope of the Northwest Caucasus Mountains, Russia (1,260-2,480 m a.s.l.), in mixed, fir, and deciduous forests, as well as subalpine and alpine meadows. R_S was measured simultaneously in each ecosystem at 12 randomly distributed points using the closed static chamber technique. After the measurements, topsoil samples (0-10 cm) were collected under each chamber (n = 60). Several soil physicochemical, microbial, and vegetation indices were assessed as potential drivers of R_S. We tested two hypotheses: (i) the spatial variability of R_S is higher in forests than in grasslands; and (ii) the spatial variability of R_S in forests is mainly due to soil microbial activity, whereas in grasslands, it is mainly due to vegetation characteristics. Unexpectedly, R_S variability was lower in forests than in grasslands, ranging from 1.3-6.5 versus 3.4-12.7 μmol CO₂ m^-1 s^-1, respectively. Spatial variability of R_S in forests was related to microbial functioning through chitinase activity (50% explained variance), whereas in grasslands it was related to vegetation structure, namely graminoid abundance (27% explained variance). Apparently, the chitinase dependence of R_S variability in forests may be related to soil N limitation. This was confirmed by low N content and high C:N ratio compared to grassland soils. The greater sensitivity of grassland R_S to vegetation structure may be related to the essential root C allocation for some grasses. Thus, the first hypothesis concerning the higher spatial variability of R_S in forests than in grasslands was not confirmed, whereas the second hypothesis concerning the crucial role of soil microorganisms in forests and vegetation in grasslands as drivers of R_S spatial variability was confirmed.

Spatial variation in forest soil respiration: A systematic review of field observations at the global scale

As it is responsible for the second largest CO₂ flux in the terrestrial ecosystem, the accurate estimation and prediction of soil respiration (SR) are necessary, especially for forest ecosystems, which are a major contributor to the total terrestrial SR. Spatial variation is one of the challenges affecting the accurate estimation and prediction of forest SR in ecosystems. Although a number of studies have examined spatial variation in SR within individual forests, the magnitude and patterns of spatial variation in SR within forest ecosystems (CV of SR [%]) remain unexplored at the global scale. In this study, we collected 94 field observation studies to demonstrate the range and pattern of the CV of SR, and to clarify the controlling factors. Through our analysis, the CV of SR was found to range from 1.8 % to 89.3 % on the global scale; it was highest in the equatorial zone (39.0 % ± 13.8 %) and followed by the warm temperate zone (32.6 ± 14.5 %) and the snow zone (30.0 % ± 16.3 %). There was a significant negative correlation between the CV of SR and soil water content, bulk density, fine root biomass, and elevation at both the global scale and in each climatic zone (P < 0.01). Other factors such as total nitrogen content, mean of diameter at breast height, slope, etc., were also significantly correlated with the CV of SR, but the correlation was different among climatic zones. This study provides an overall perspective of the CV of SR by clarifying the range, patterns, and controlling factors at both the global scale and in each climatic zone. However, further research is needed, especially regarding the mechanisms between the CV of SR and its controlling factors.

Controls and variability of soil respiration temperature sensitivity across China

Understanding the temperature sensitivity (Q₁₀) of soil respiration is critical for benchmarking the potential intensity of regional and global terrestrial soil carbon fluxes-climate feedbacks. Although field observations have demonstrated the strong spatial heterogeneity of Q₁₀, a significant knowledge gap still exists regarding to the factors driving spatial and temporal variabilities of Q₁₀ at regional scales. Therefore, we used a machine learning approach to predict Q₁₀ from 1994 to 2016 with a spatial resolution of 1 km across China from 515 field observations at 5 cm soil depth using climate, soil and vegetation variables. Predicted Q₁₀ varied from 1.54 to 4.17, with an area-weighted average of 2.52. There was no significant temporal trend for Q10 (p = 0.32), but annual vegetation production (indicated by normalized difference vegetation index, NDVI) was positively correlated to it (p < 0.01). Spatially, soil organic carbon (SOC) was the most important driving factor in 62 % of the land area across China, and varied greatly, demonstrating soil controls on the spatial pattern of Q₁₀. These findings highlighted different environmental controls on the spatial and temporal pattern of soil respiration Q₁₀, which should be considered to improve global biogeochemical models used to predict the spatial and temporal patterns of soil carbon fluxes to ongoing climate change.

Tidal organic input restricts CO2 sequestration capacity of estuarine wetlands

The inland and estuary wetlands that characterized by different natural environment perform distinctly in soil carbon (C) sink. It was deemed that estuary wetland has a higher organic C accumulation rate than inland wetland, due to its higher primary production and tidal organics input, thus having higher organic C sink capacity. While from CO₂ budge in view, whether does the large organic input from tide restrict CO₂ sequestration capacity of estuary wetland has not been discussed comparing with inland wetland. In this study, inland and estuary wetlands were selected to study the potential of CO₂ sequestration capacity. It was found that inland wetland had most of soil organic carbon (SOC) derived from plant C, which brought remarkable organic C content and nourished higher microbial biomass, dehydrogenase, and β_glucosidase than estuary wetland. The estuary wetland instead accumulated less SOC, a considerable proportion of which came from tidal waters, therefore supporting lower microbial biomass and enzyme activities than that in inland wetland. However, estuary wetland was evaluated having higher capability in SOC mineralization than inland wetland in consideration of soil respiration (SR) and SR quotient. It was concluded that tidal organic C accelerated the SOC mineralization in estuarine wetland, thus weakening the CO₂ sequestration. These results implied the importance of pollution control for reservation CO₂ sink function in estuarine wetland.

Multiple factors co-limit short-term in situ soil carbon dioxide emissions

Soil respiration is a major source of atmospheric CO2. If it increases with warming, it will counteract efforts to minimize climate change. To improve understanding of environmental controls over soil CO2 emission, we applied generalized linear modeling to a large dataset of in situ measurements of short-term soil respiration rate, with associated environmental attributes, which was gathered over multiple years from four locations that varied in climate, soil type, and vegetation. Soil respiration includes many CO2-producing processes: we theorized that different environmental factors could limit each process distinctly, thereby diminishing overall CO2 emissions. A baseline model that included soil temperature, soil volumetric water content, and their interaction was effective in estimating soil respiration at all four locations (p < 0.0001). Model fits, based on model log likelihoods, improved continuously as additional covariates were added, including mean daily air temperature, enhanced vegetation index (EVI), and quadratic terms for soil temperature and water content, and their interactions. The addition of land cover and its direct interactions with environmental variables further improved model fits. Significant interactions between covariates were observed at each location and at every stage of analysis, but the interaction terms varied among sites and models, and did not consistently maintain importance in more complex models. A main-effects model was therefore tested, which included soil temperature and water content, their quadratic effects, EVI, and air temperature, but no interactions. In that case all six covariates were significant (p < 0.0001) when applied across sites. We infer that local-scale soil-CO2 emissions are commonly co-limited by EVI and air temperature, in addition to soil temperature and water content. Importantly, the quadratic soil temperature and moisture terms were significantly negative: estimated soil-CO2 emissions declined when soil temperature exceeded 22.5°C, and as soil moisture differed from the optimum of 0.27 m3 m-3.

Nitrate as an alternative electron acceptor destabilizes the mineral associated organic carbon in moisturized deep soil depths

Numerous studies have investigated the effects of nitrogen (N) addition on soil organic carbon (SOC) decomposition. However, most studies have focused on the shallow top soils <0.2 m (surface soil), with a few studies also examining the deeper soil depths of 0.5-1.0 m (subsoil). Studies investigating the effects of N addition on SOC decomposition in soil >1.0 m deep (deep soil) are rare. Here, we investigated the effects and the underlying mechanisms of nitrate addition on SOC stability in soil depths deeper than 1.0 m. The results showed that nitrate addition promoted deep soil respiration if the stoichiometric mole ratio of nitrate to O₂ exceeded the threshold of 6:1, at which nitrate can be used as an alternative acceptor to O₂ for microbial respiration. In addition, the mole ratio of the produced CO₂ to N₂O was 2.57:1, which is close to the theoretical ratio of 2:1 expected when nitrate is used as an electron acceptor for microbial respiration. These results demonstrated that nitrate, as an alternative acceptor to O₂, promoted microbial carbon decomposition in deep soil. Furthermore, our results showed that nitrate addition increased the abundance of SOC decomposers and the expressions of their functional genes, and concurrently decreased MAOC, and the ratio of MAOC/SOC decreased from 20% before incubation to 4% at the end of incubation. Thus, nitrate can destabilize the MAOC in deep soils by stimulating microbial utilization of MAOC. Our results imply a new mechanism on how above-ground anthropogenic N inputs affect MAOC stability in deep soil. Mitigation of nitrate leaching is expected to benefit the conservation of MAOC in deep soil depths.

Biochar-plant interaction and detoxification strategies under abiotic stresses for achieving agricultural resilience: A critical review

The unpredictable climatic perturbations, the expanding industrial and mining sectors, excessive agrochemicals, greater reliance on wastewater usage in cultivation, and landfill leachates, are collectively causing land degradation and affecting cultivation, thereby reducing food production globally. Biochar can generally mitigate the unfavourable effects brought about by climatic perturbations (drought, waterlogging) and degraded soils to sustain crop production. It can also reduce the bioavailability and phytotoxicity of pollutants in contaminated soils via the immobilization of inorganic and/or organic contaminants, commonly through surface complexation, electrostatic attraction, ion exchange, adsorption, and co-precipitation. When biochar is applied to soil, it typically neutralizes soil acidity, enhances cation exchange capacity, water holding capacity, soil aeration, and microbial activity. Thus, biochar has been was widely used as an amendment to ameliorate crop abiotic/biotic stress. This review discusses the effects of biochar addition under certain unfavourable conditions (salinity, drought, flooding and heavy metal stress) to improve plant resilience undergoing these perturbations. Biochar applied with other stimulants like compost, humic acid, phytohormones, microbes and nanoparticles could be synergistic in some situation to enhance plant resilience and survivorship in especially saline, waterlogged and arid conditions. Overall, biochar can provide an effective and low-cost solution, especially in nutrient-poor and highly degraded soils to sustain plant cultivation.

Steering restoration of coal mining degraded ecosystem to achieve sustainable development goal-13 (climate action): United Nations decade of ecosystem restoration (2021-2030)

For millennium, mining sector is a source not only of mineral extraction for industrialization, economic expansion, and urban sprawling, but also of socio-environmental concern. It, therefore, has been the central attention of the business and public policy sustainable development scheme for several years. Thus, gradually, mining industries are getting involved with the concerns such as carbon emissions mitigation and carbon accounting to govern a rhetorical shift towards "sustainable mining". However, there is scarce knowledge about how the emergence of a "green and self-sustaining" forestry reclamation strategy coupled with potential carbon sequestration capacity in degraded mining areas will be an impeccable option for achieving sustainable development goal-13 (SDG-13: climate action) and ecosystem services during United Nation decade of ecosystem restoration. This paper reviews the extent to which reforestation and sustainable land management practices that employed to enhance ecosystem carbon pool and atmospheric CO2 sequestration capacity to offset CO2 emission and SOC (soil organic carbon) losses, as consequences of coal mining, to partially mitigate global climate crisis. Moreover, future research is required on mining innovation concepts and its challenges for designing an SDG impact framework, so that it not only synergies amongst SDGs, but also trade-offs between each individual "politically legitimized post-2015 development agenda" (i.e. UNSDGs) could be depicted in a systematic way. In a developing country like India, it is also an utmost need to assess the environmental impact and economic performance of such technological innovation and its possible synergistic effect.

Seasonal and diurnal soil respiration dynamics under different land management practices in the sub-tropical highland agroecology of Ethiopia

The temporal dynamics of soil respiration change in response to different land management practices are not well documented. This study investigated the effects of soil bunds on the monthly and diurnal dynamics of soil respiration rates in the highlands of the Upper Blue Nile basin in Ethiopia. Six plots (with and without soil bunds, three replicates) were used for measurement of seasonal soil respiration, and 18 plots were used for measurement of diurnal soil respiration. We collected seasonal variation data on a monthly basis from September 2020 to August 2021. Diurnal soil respiration data were collected four times daily (5 a.m., 11 a.m., 5 p.m., and 11 p.m.) for 2 weeks from 16 to 29 September 2021. A Wilcoxon signed-rank test showed that seasonal soil respiration rates differed significantly (p < 0.05) between soil bund and control plots in all seasons. In plots with soil bunds, seasonal soil respiration rates were lowest in February (1.89 ± 0.3 µmol CO₂ m^-2 s^-1, mean ± SE) and highest in October (14.54 ± 0.5 µmol CO₂ m^-2 s^-1). The diurnal soil respiration rate was significantly (p < 0.05) higher at 11 a.m. than at other times, and was lowest at 5 a.m. Seasonal variation in soil respiration was influenced by soil temperature negatively and moisture positively. Diurnal soil respiration was significantly affected by soil temperature but not by soil moisture. Further study is required to explore how differences in soil microorganisms between different land management practices affect soil respiration rates.

Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models

Soil heterotrophic respiration (R_h ) refers to the flux of CO₂ released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, large uncertainties still exist in the estimation of global R_h , which further limits our current understanding of carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global data set of soil R_h , by linking 761 field observations with both abiotic and biotic predictors. We estimated that global R_h was 48.8 ± 0.9 Pg C year^-1 for 1982-2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C year^-1 ) of 16 terrestrial ecosystem models. By integrating our observational R_h with independent soil carbon stock data sets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 year. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land-use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted R_h values to obtain reliable projections of the carbon sink capacity of terrestrial ecosystems.

Impact of Natural Forest Succession on Changes in Soil Organic Carbon in the Polish Carpathian Mountains

The main driver of the Carpathian landscape is the process of natural forest succession on the semi-natural meadows unique to the region. Moreover, these semi-natural mountain meadows contribute to ecosystem services, although increasing forest areas are recommended by current international policy agendas. The purpose of this study was to examine the impact of natural forest succession in the Polish part of Carpathian on changes in soil organic carbon and assess the influence of different soil properties on organic carbon content across three land uses. Soil samples were taken from 10 transects consisting of semi-natural mountain meadow, natural successional forest and old-growth forest, selected in three Polish Carpathian national parks. Measurements of organic carbon, dissolved organic carbon, microbial properties, such as microbial respiration, and enzyme activities were made; additionally, biochemical indicators were calculated. To describe the influence of measured soil parameters and calculated indicators of soil organic carbon changes, the organic carbon dependent variable regression equations across all studied soils and for the individual land use and examined layers were evaluated. The overall regression equation indicated that changes in organic carbon general to all investigated soils depended on microbial biomass carbon content, microbial quotient, dissolved organic carbon content and metabolic quotient. The regression models obtained for the individual land use variants and soil layers explained 77% to 99% of the variation in organic carbon. Results showed that natural forest succession caused a decrease in microbial biomass carbon content, and successional forest soils characterized less efficient use of organic substrates by microbial biomass.

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R_S), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R_S are irreconcilable. When we estimate GPP based on R_S measurements and some assumptions about R_S:GPP ratios, we found the resulted global GPP values (bootstrap mean [Formula: see text] Pg C yr^-1) are significantly higher than most GPP estimates reported in the literature ([Formula: see text] Pg C yr^-1). Similarly, historical GPP estimates imply a soil respiration flux (Rs_GPP, bootstrap mean of [Formula: see text] Pg C yr^-1) statistically inconsistent with most published R_S values ([Formula: see text] Pg C yr^-1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R_S:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems

Belowground autotrophic respiration (RA_soil) depends on carbohydrates from photosynthesis flowing to roots and rhizospheres, and is one of the most important but least understood components in forest carbon cycling. Carbon allocation plays an important role in forest carbon cycling and reflects forest adaptation to changing environmental conditions. However, carbon allocation to RA_soil has not been fully examined at the global scale. To fill this knowledge gap, we first used a Random Forest algorithm to predict the spatio-temporal patterns of RA_soil from 1981 to 2017 based on the most updated Global Soil Respiration Database (v5) with global environmental variables; calculated carbon allocation from photosynthesis to RA_soil (CA_B) as a fraction of gross primary production; and assessed its temporal and spatial patterns in global forest ecosystems. Globally, mean RA_soil from forests was 8.9 ± 0.08 Pg C yr^-1 (mean ± standard deviation) from 1981 to 2017 and increased significantly at a rate of 0.006 Pg C yr^-2, paralleling broader soil respiration changes and suggesting increasing carbon respired by roots. Mean CA_B was 0.243 ± 0.016 and decreased over time. The temporal trend of CA_B varied greatly in space, reflecting uneven responses of CA_B to environmental changes. Combined with carbon use efficiency, our CA_B results offer a completely independent approach to quantify global aboveground autotropic respiration spatially and temporally, and could provide crucial insights into carbon flux partitioning and global carbon cycling under climate change.

Analysis of soil respiration and influencing factors in a semiarid dune-meadow cascade ecosystem

The characteristics of soil respiration (R_s) in semiarid regions are important with regard to the carbon cycle of complex underlying surfaces and estimation of carbon emissions from regional ecosystems. During the growing season (May-September 2016), in situ observations of R_s were obtained concurrently with measurements of soil bacteria (B_s), soil moisture (M_s), and soil temperature (T_s) at depths of 0-10 cm, in a dune-meadow cascade ecosystem. Results showed that R_s differences among the various ecosystems were significant (P < 0.01), the intensity of R_s in meadows was twice stronger than that in dunes. The average values of R_s presenting a declined trend follows MPA (11.19 μmol m^-2 s^-1) > MAF (7.75) > SSG (6.78) > SMAH (5.02) > SFAH (4.8) > FLC (4.28) > SBG (3.09). An extremely significant (p < 0.01) positive correlated power relationship can be found between R_s and B_s, which could explain 62.41%-86.56% of the variation in R_s in the various ecosystems. Field capacity and the saturated water content were the key demarcation points for the interactive relationship between R_s and M_s, which showing a significant (P < 0.05) positive correlated power relationship in dunes, in contrast, it presenting a significant (P < 0.05) negative correlated exponential relationship in meadows. R_s was positively exponentially correlated with T_s, significant (P < 0.05) in meadows and nonsignificant (P > 0.05) in dunes. Future research should be strengthened to consider multiple growing seasons experiencing various climatic conditions for accurate estimation of terrestrial carbon emissions in arid and semiarid ecosystems.

Spatio-temporal variability in soil CO2 efflux and regulatory physicochemical parameters from the tropical urban natural and anthropogenic land use classes

Urban ecosystems, the heterogeneous and rapidly changing landscape, showed a considerable impact on the global C cycle. However, studies encompassing the spatial differences in urban land uses on soil C dynamics are limited in tropical ecosystems. In this study, seasonal and temporal variability in soil CO₂ efflux (SCE) and its regulatory physicochemical variables under five urban land use classes viz., Bare (BAR), Agriculture (AGR), Plantation (PLT), Grassland (GRA) and Lawns (LAW) were assessed from 2014 to 2016. Bare land use was considered as the reference for observing the variation for different land uses. Seasonal measurements of SCE, soil temperature, moisture content, pH, ammonium-N, nitrate-N and microbial biomass C (MBC) were performed whereas soil organic C (SOC), soil N, and soil physical properties were measured annually. Our results showed a significant (P < 0.01) increase in SCE by 89%, 117%, 132% and 166% for land use types from BAR to AGR, PLT, GRA and LAW, respectively. The results revealed a two-fold increase in SCE from anthropogenically managed urban lawns as compared to bare soil. PLT and LAW land use classes showed higher SOC and N contents. SCE was found positively correlated with temperature, moisture, SOC, soil N and MBC whereas negatively correlated with ammonium-N and nitrate-N (at P < 0.05) for the overall dataset. Soil moisture, temperature, SOC, porosity and pH were identified as the major determinant of urban SCE by explaining 63% of the variability in overall SCE. Further, temperature for BAR and LAW; moisture for PLT; ammonium-N for GRA; and nitrate-N for AGR were identified as the major regulators of SCE for different land use classes. The findings revealed that the interaction of soil temperature and moisture with nutrient availability regulates overall and seasonal variability in SCE in an urban ecosystem. Since these variables are highly affected by climate change, thus, the soil C source-sink relationships in tropical urban ecosystems may further change and induce a positive global warming potential from urban ecosystems.

Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

Soil respiration (Rs), the efflux of CO₂ from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year^-1 , and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year^-1 , whereas parameterization using SRDBv5 yielded 96.5 Pg C year^-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year^-1 . Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C year^-1 , respectively. SRDBv5's inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 ± 21.4 Pg C year^-1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.

Real-Time Mass Spectrometry Measurements of Respiration Rates in Biological Systems

Many organisms process carbon and other nutrients to generate energy through aerobic respiration where organic carbon compounds are broken down and oxygen is consumed, producing carbon dioxide and water. Respiration is indicative of active metabolism, and respiration rates are proportional to the amount of living biomass in an ecosystem. Although there are many methods for measuring respiration rates in the laboratory, current systems, such as infrared gas analyzers, are limited in their ability to independently resolve isotopomer fluxes across a range of relevant gases including both CO₂ and O₂ in real-time. Therefore, monitoring of biological respiration in real time under controlled laboratory conditions would enable better understanding of cellular physiology. To address this challenge, we developed a real time mass spectrometry (RTMS) manifold that simultaneously measures production and consumption of multiple gases and their isotopologues in seconds with the speed and sensitivity necessary to characterize rapidly changing respiration events as they occur. This universal manifold can be fitted to a variety of instruments and affords the same analytical precision and accuracy of the instrument while allowing for the real time measurements. Here, we paired the manifold to a single quad MS with an electron impact (EI) source operated in scan mode to detect extracted target gases by their respective masses (e.g., ¹²CO₂ at mass 44, ¹³CO₂ at 45). We demonstrated applicability of the RTMS instrument to different biological ecosystems (bacterial cultures, plants, and soil), and in all cases, we were able to detect simultaneous and rapid measurements of multiple gases in real time, providing novel insights into complex respiratory metabolism and the influence of biological and environmental factors.

Dynamics of the soil respiration response to soil reclamation in a coastal wetland

The soil carbon (C) pools in coastal wetlands are known as "blue C" and have been damaged extensively owing to climate change and land reclamation. Because soil respiration (RS) is the primary mechanism through which soil carbon is released into the atmosphere at a global scale, investigating the dynamic characteristics of the soil respiration rate in reclaimed coastal wetlands is necessary to understand its important role in maintaining the global C cycle. In the present study, seasonal and diurnal changes in soil respiration were monitored in one bare wetland (CK) and two reclaimed wetlands (CT, a cotton monoculture pattern, and WM, a wheat-maize continuous cropping pattern) in the Yellow River Delta. At the diurnal scale, the RS at the three study sites displayed single-peak curves, with the lowest values occurring at midnight (00:00 a.m.) and the highest values occurring at midday (12:00 a.m.). At the seasonal scale, the mean diurnal RS of the CK, CT and WM in April was 0.24, 0.26 and 0.79 μmol CO₂ m^-2 s^-1, and it increased to a peak in August for these areas. Bare wetland conversion to croplands significantly elevated the soil organic carbon (SOC) pool. The magnitude of the RS was significantly different at the three sites, and the yearly total amounts of CO₂ efflux were 375, 513 and 944 g CO₂·m^-2 for the CK, CT and WM, respectively. At the three study sites, the surface soil temperature had a significant and positive relationship to the RS at both the diurnal and seasonal scales, and it accounted for 20-52% of the seasonal variation in the daytime RS. The soil water content showed a significant but negative relationship to the RS on diurnal scale only at the CK site, while it significantly increased with the RS on seasonal scale at all study sites. Although the RS showed a noticeable relationship to the combination of soil temperature and water content, the synergic effects of these two environment factors were not much higher than the individual effects. In addition, the correlation analysis showed that the RS was also influenced by the soil physico-chemical properties and that the soil total nitrogen had a closer positive relationship to the RS than the other nutrients, indicating that the soil nitrogen content plays a more important role in promoting carbon loss.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

The eddy covariance (EC) technique was used to measure variations of orchard-atmosphere CO₂ exchange, as a function of meteorological variables in an apple orchard in 2016-2017. The annual average CO₂ exchange rate was 2.295 kg m^-2. Excavations and biomass assessments demonstrated that the orchard stored close to 20.6 tC ha^-1 as plant C over a 15-year period. Seasonally, high rates of CO₂ uptake and low CO₂ emissions occurred between May and August and December and March, respectively. The maximum rates of monthly CO₂ exchange were 144.44 and 153.61 gC m^-2 month^-1 in August 2016 and June 2017, respectively. Partial least squares (PLS) regressions were used to analyze the influence of meteorological factors to on CO₂ exchange rates. Temperature and photosynthetic active radiation (PAR) were observed to exert the largest influence on driving variation in CO₂ exchange. The main meteorological factors affecting CO₂ exchange on daily and monthly time scales were soil temperature (T_soil), air temperature (T_a), PAR, below canopy CO₂ concentration (BCC), vapor pressure deficit (VPD), and soil water content at 50 cm (SWC_50cm). The regression model equation describing CO₂ exchange included T_a, VPD, precipitation (PPT), and sunshine duration (SD), as significant variables. This model curve fitting explains over 80% of the variation in CO₂ exchange. This study provides CO₂ exchange characteristics and a model equation capable of predicting CO₂ exchange of an apple orchard. Graphical Abstract.

Accounting for soil respiration variability – Case study in a Mediterranean pine-dominated forest

The number of spots to monitor to evaluate soil respiration (R_s) is often chosen on an empirical or conventional basis. To obtain an insight into the necessary number of spots to account for R_s variability in a Mediterranean pine-dominated mixed forest, we measured R_s all year long on sixteen dates with a portable gas-analyser in 50 spots per date within an area 1/3 ha wide. Linear mixed-effects models with soil temperature and litter moisture as descriptors, were fitted to the collected data and then evaluated in a Monte Carlo simulation on a progressively decreasing number of spots to identify the minimum number required to estimate R_s with a given confidence interval. We found that monitoring less than 14 spots would have resulted in a 10% probability of not fitting the model, while monitoring 20 spots would have reduced the same probability to about 5% and was the best compromise between field efforts and quality of the results. A simple rainfall index functional to select sampling dates during the summer drought is proposed.

COSORE: A community database for continuous soil respiration and other soil-atmosphere greenhouse gas flux data

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO₂ flux, commonly though imprecisely termed soil respiration (R_S ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency R_S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R_S , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Soil respiration (R _s) represents the largest flux of CO₂ from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R _s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R _s, we showed that land-cover change played a more important role in regulating R _s changes in temperate and boreal regions during 2000-2014. Significant changes in R _s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R _s patterns and highlight the importance of land-cover change in driving global and regional R _s changes.

Effect of Fast-Growing Trees on Soil Properties and Carbon Storage in an Afforested Coal Mine Land (India)

Surface coal mining activities have numerous consequences on terrestrial ecosystems. Loss of soil and biomass carbon pool due to mining activities is a serious concern in the rapidly changing environment. We investigated the effect of fast-growing trees (Albizia lebbeck, Albizia procera, and Dalbergia sissoo) on soil fertility and ecosystem carbon pool after eight years of afforestation in the post-mining land of Jharia coalfield, India, and compared with the adjacent natural forest site. Significant differences in soil organic carbon (SOC) and total nitrogen (TN) stocks in afforested mine soil and natural forest soils were observed. Greater SOC stock was found under D. sissoo (30.17 Mg·C·ha−1) while total N stock was highest under A. lebbeck (4.16 Mg·N·ha−1) plantation. Plant biomass accumulated 85% of the natural forest carbon pool after eight years of afforestation. The study concluded that planting fast-growing trees in post-mining lands could produce a promising effect on mine soil fertility and greater carbon storage in a short period.

Blind spots in global soil biodiversity and ecosystem function research

Soils harbor a substantial fraction of the world's biodiversity, contributing to many crucial ecosystem functions. It is thus essential to identify general macroecological patterns related to the distribution and functioning of soil organisms to support their conservation and consideration by governance. These macroecological analyses need to represent the diversity of environmental conditions that can be found worldwide. Here we identify and characterize existing environmental gaps in soil taxa and ecosystem functioning data across soil macroecological studies and 17,186 sampling sites across the globe. These data gaps include important spatial, environmental, taxonomic, and functional gaps, and an almost complete absence of temporally explicit data. We also identify the limitations of soil macroecological studies to explore general patterns in soil biodiversity-ecosystem functioning relationships, with only 0.3% of all sampling sites having both information about biodiversity and function, although with different taxonomic groups and functions at each site. Based on this information, we provide clear priorities to support and expand soil macroecological research.

Inorganic Nitrogen Addition Affects Soil Respiration and Belowground Organic Carbon Fraction for a Pinus tabuliformis Forest

The capability of forest ecosystems to sequester carbon from the atmosphere largely depends on the interaction of soil organic matter and nitrogen, and thus, this process will be greatly influenced by nitrogen deposition under the future scenario of global change. To clarify this interaction, the current study explored the variations in soil carbon fraction and soil respiration with different levels of nitrogen deposition. NH4NO3 was added at concentrations of 0, 50, 100, 200, and 400 kg N ha−1 year−1 separately on twenty 100 m2 plots in a Pinus tabuliformis Carr forest in northern China. Soil samples were analyzed for their nutrient content and biophysical properties two years after nitrogen application, and the soil respiration rate was measured every month during the study period. Seasonal variation and nitrogen addition significantly affected soil respiration rate. On average, nitrogen addition significantly reduced the annual soil respiration rate by 23.74%. Fine root biomass significantly decreased by an average of 43.55% in nitrogen treatment plots compared to the control plot. However, the average proportions of autumn and winter soil respiration rates out of the annual cumulative soil respiration rate greatly increased from 23.57% and 11.04% to 25.90% and 12.18%, respectively. The soil microbial biomass carbon content in the control plot was 342.39 mg kg−1, 23.50% higher than the average value in nitrogen treatment plots. The soil dissolved organic carbon was reduced by 22.60%, on average, following nitrogen addition. Significant correlations were detected between fine root biomass and the annual cumulative soil respiration rate, soil microbial biomass carbon content, and soil dissolved organic carbon content. This demonstrates that nitrogen addition affects soil organic carbon transformation and carbon emission, mainly by depressing fine root production.

Interactive effects of hydrological conditions on soil respiration in China's Horqin sandy land: An example of dune-meadow cascade ecosystem

Soil moisture (M_s) strongly influences dynamic changes in soil respiration (R_s) and is thus an important factor when predicting soil carbon emissions. However, the various sources of M_s (rainfall, groundwater, and condensation) exert complicated and uncertain effects on R_s. This study examined the growth seasonal variation (from April to October) of R_s and the diurnal variation in a cascade ecosystem consisting of sandy bare ground, a transitional artificial Populus forest, and a meadow Phragmites communis community in China's Horqin sandy land. Simultaneous measurements of the 0-10 cm depth soil temperature (T_s) and M_s, rainfall, the surface air relative humidity and the groundwater depth were collected. The results revealed that in sandy bare ground with M_s below field capacity, M_s had a greater impact on R_s than T_s, and rainfall could increase R_s. The effect of condensation on R_s during periods of continuous drought could not be ignored. In the meadowlands with M_s above field capacity, the groundwater affected R_s indirectly by regulating M_s and the relationship with T_s, and rainfall had an adverse effect on R_s. The effects of rainfall, M_s and T_s on R_s were minimum as M_s approached the saturation water content. In the transitional forest, M_s and T_s were the main factors controlling R_s. The most favorable M_s for R_s was close to the field capacity. The results emphasize that field capacity and saturation water content are the demarcation points of a soil carbon emissions prediction model, and the effect of different hydrological conditions and T_s on R_s at each segment are reconsidered accordingly. Ultimately, the carbon emission patterns of the cascade ecosystems in arid and semi-arid areas are extremely complicated and have to be considered specially for estimating terrestrial carbon emissions.

Environmental and Vegetative Controls on Soil CO2 Efflux in Three Semiarid Ecosystems

Soil CO2 efflux (Fsoil) is a major component of the ecosystem carbon balance. Globally expansive semiarid ecosystems have been shown to influence the trend and interannual variability of the terrestrial carbon sink. Modeling Fsoil in water-limited ecosystems remains relatively difficult due to high spatial and temporal variability associated with dynamics in moisture availability and biological activity. Measurements of the processes underlying variability in Fsoil can help evaluate Fsoil models for water-limited ecosystems. Here we combine automated soil chamber and flux tower data with models to investigate how soil temperature (Ts), soil moisture (θ), and gross ecosystem photosynthesis (GEP) control Fsoil in semiarid ecosystems with similar climates and different vegetation types. Across grassland, shrubland, and savanna sites, θ regulated the relationship between Fsoil and Ts, and GEP influenced Fsoil magnitude. Thus, the combination of Ts, θ, and GEP controlled rates and patterns of Fsoil. In a root exclusion experiment at the grassland, we found that growing season autotrophic respiration accounted for 45% of Fsoil. Our modeling results indicate that a combination of Ts, θ, and GEP terms is required to model spatial and temporal dynamics in Fsoil, particularly in deeper-rooted shrublands and savannas where coupling between GEP and shallow θ is weaker than in grasslands. Together, these results highlight that including θ and GEP in Fsoil models can help reduce uncertainty in semiarid ecosystem carbon dynamics.

SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring

Estimation of regional soil carbon flux is very important for the study of the global carbon cycle. The spatial heterogeneity of soil respiration prevents the actual status of regional soil carbon flux from being revealed by measurements of only one or a few spatial sampling positions, which are usually used by traditional studies for the limitation of measurement instruments, so measuring in many spatial positions is very necessary. However, the existing instruments are expensive and cannot communicate with each other, which prevents them from meeting the requirement of synchronous measurements in multiple positions. Therefore, we designed and implemented an instrument for soil carbon flux measuring based on dynamic chamber method, SCFSen, which can measure soil carbon flux and communicate with each other to construct a sensor network. In its working stage, a SCFSen node measures the concentration of carbon in the chamber with an infrared carbon dioxide sensor for certain times periodically, and then the changing rate of the measurements is calculated, which can be converted to the corresponding value of soil carbon flux in the position during the short period. A wireless sensor network system using SCFSens as soil carbon flux sensing nodes can carry out multi-position measurements synchronously, so as to obtain the spatial heterogeneity of soil respiration. Furthermore, the sustainability of such a wireless sensor network system makes the temporal variability of regional soil carbon flux can also be obtained. So SCFSen makes thorough monitoring and accurate estimation of regional soil carbon flux become more feasible.

Constraining estimates of global soil respiration by quantifying sources of variability

Quantifying global soil respiration (R_SG ) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of R_SG range from 68 to 98 Pg C year^-1 , causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain R_SG , we developed R_SG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (R_S ) and climate data to predict R_SG . From the resulting R_SG estimates (range = 66.62-100.72 Pg), we calculated variability associated with each assumption. Among model formats, using monthly R_S data rather than annual data decreased R_SG by 7.43-9.46 Pg; however, R_SG calculated from daily R_S data was only 1.83 Pg lower than the R_SG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36 Pg C differences, respectively. If the timescale of R_S data is constant, R_SG estimated by the first-order exponential (93.2 Pg) was greater than the Random Forest (78.76 Pg) or second-order exponential (76.18 Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to R_SG. The results indicated R_SG is lower than in recent papers and the current benchmark for land models (98 Pg C year^-1 ), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.

Globally rising soil heterotrophic respiration over recent decades

Global soils store at least twice as much carbon as Earth's atmosphere^1,2. The global soil-to-atmosphere (or total soil respiration, R_S) carbon dioxide (CO₂) flux is increasing^3,4, but the degree to which climate change will stimulate carbon losses from soils as a result of heterotrophic respiration (R_H) remains highly uncertain^5-8. Here we use an updated global soil respiration database⁹ to show that the observed soil surface R_H:R_S ratio increased significantly, from 0.54 to 0.63, between 1990 and 2014 (P = 0.009). Three additional lines of evidence provide support for this finding. By analysing two separate global gross primary production datasets^10,11, we find that the ratios of both R_H and R_S to gross primary production have increased over time. Similarly, significant increases in R_H are observed against the longest available solar-induced chlorophyll fluorescence global dataset, as well as gross primary production computed by an ensemble of global land models. We also show that the ratio of night-time net ecosystem exchange to gross primary production is rising across the FLUXNET2015¹² dataset. All trends are robust to sampling variability in ecosystem type, disturbance, methodology, CO₂ fertilization effects and mean climate. Taken together, our findings provide observational evidence that global R_H is rising, probably in response to environmental changes, consistent with meta-analyses^13-16 and long-term experiments¹⁷. This suggests that climate-driven losses of soil carbon are currently occurring across many ecosystems, with a detectable and sustained trend emerging at the global scale.